Detecting unusual activities in surveillance video.
Translated title
Detecting unusual activities in surveillance video.
Author
Term
4. term
Publication year
2012
Submitted on
2012-05-30
Pages
89
Abstract
This master thesis describes a system for detecting abnormalities in video surveillance by using motion, size, texture and direction features. The method is based on an existing solution, but includes improvements by using a different optical flow algorithm. The method is tested on the publicly available UCSD anomaly detection dataset with good results within the different categories compared to other methods. Tests have been concluded to view the results of motion, size and texture features independently where the latter have shown to be ineffective. Two datasets were created for direction based abnormalities. The results on these are very good with an error rate of 0.3% which with small alterations could be used in surveillance systems. Further research should be put on applicating the methods for video surveillance systems and improving the size and texture feature.
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